└── README.md /README.md: -------------------------------------------------------------------------------- 1 | # Graph-Neural-Networks 2 | All materials related to GNN 3 | 4 | ## Related survey papers 5 | * Deep Learning on Graphs: A Survey, arXiv 2018 6 | * A Comprehensive Survey on Graph Neural Networks,arXiv 2018 7 | * Graph Neural Networks: A Review of Methods and Applications,arXiv 2018 8 | * Relational inductive biases, deep learning, and graph networks,arXiv 2018 9 | 10 | 11 | ## Motivation of GNN 12 | * The first motivation of GNNs roots in convolutional neural networks (CNNs) 13 | * The other motivation comes from graph embedding, which learns to represent graph nodes, edges or subgraphs in low-dimensional vectors. 14 | 15 | ## GNN worth investigating 16 | * GNNs propagate on each node respectively, ignoring the input order of nodes 17 | * GNNs can do propagation guided by the graph structure instead of using it as part of features 18 | * GNNs explore to generate the graph from non structural data like scene pictures and story documents, which can be a powerful neural model for further high-level AI. 19 | 20 | ## Challenges of traditional deep learning on graphs 21 | * Irregular domain 22 | * Varying structures and tasks 23 | * Scalability and parallelization 24 | * Interdiscipline 25 | 26 | ## General Frameworks 27 | * Message Passing Neural Networks(MPNN) 28 | * Non-local Neural Networks(NLNN) 29 | * Graph Networks(GN) 30 | 31 | ## Taxonomy of Deep Learning methods on graphs 32 | * Graph Neural Networks 33 | * Graph Convolutional Networks 34 | * Spectral-based 35 | * Spatial-based 36 | * Pooling modules 37 | * Graph Auto-encoders 38 | * Auto-encoders 39 | * Variational Auto-encoders 40 | * Graph Attention Networks 41 | * Graph Generative Networks 42 | * Graph Spatial-Temporal Networks 43 | * Graph Recurrent Neural Networks 44 | * Graph Reinforcement Learning 45 | 46 | ## Datasets 47 | * Citation Networks 48 | * Cora (Collective classification in network data,AI magazine,2008) 49 | * Citeseer (Collective classification in network data,AI magazine,2008) 50 | * Pubmed (Collective classification in network data,AI magazine,2008) 51 | * [DBLP](aminer.org/citation) 52 | * Social Networks 53 | * BlogCatalog (Relational learning via latent social dimensions,KDD 2009) 54 | * Reddit (representation learning on large graphs,NIPS 2017) 55 | * [Epinions](www.epinions.com) 56 | * Chemical/Biological Graphs 57 | * PPI (Predicting multicellular function through multi-layer tissue networks,Bioinformatics 2017) 58 | * NCI-1 (Comparison of descriptor spaces for chemical compound retrieval and classification,KIS 2008) 59 | * NCI-109 (Comparison of descriptor spaces for chemical compound retrieval and classification,KIS 2008) 60 | * MUTAG (Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro compounds. correlation with molecular orbital energies and hydrophobicity,Journal of medicinal chemistry,1991) 61 | * D&D (Distinguishing enzyme structures from non-enzymes without alignments,Journal of molecular biology 2003) 62 | * QM9 (Quantum chemistry structures and properties of 134 kilo molecules,Scientific data 2014) 63 | * [tox21](tripod.nih.gov/tox21/challenge/) 64 | * Unstructured Graphs 65 | * [MNIST](yann.lecun.com/exdb/mnist/) 66 | * [Wikipedia](www.mattmahoney.net/dc/textdata) 67 | * 20NEWS (A probabilistic analysis of the rocchio algorithm with tfidf for text categorization.Carnegie-mellon univ pittsburgh pa dept of computer science, Tech. Rep., 1996) 68 | * Others 69 | * METR-LA (Big data and its technical challenges,Communications of the ACM 2014) 70 | * [Movie-Lens1M](grouplens.org/datasets/movielens/1m/) 71 | * Nell (Toward an architecture for never-ending language learning,AAAI 2010) 72 | 73 | ## Open-source Implementations 74 | * [ChebNet](https://github.com/mdeff/cnn_graph) 75 | * [1stChebNet](https://github.com/tkipf/gcn) 76 | * [GGNNs](https://github.com/yujiali/ggnn) 77 | * [SSE](https://github.com/Hanjun-Dai/steady_state_embedding) 78 | * [GraphSage](https://github.com/williamleif/GraphSAGE) 79 | * [LGCN](https://github.com/williamleif/GraphSAGE) 80 | * [SplineCNN](https://github.com/rusty1s/pytorch_geometric) 81 | * [GAT](https://github.com/PetarV-/GAT) 82 | * [GAE](https://github.com/limaosen0/Variational-Graph-Auto-Encoders) 83 | * [ARGA](https://github.com/Ruiqi-Hu/ARGA) 84 | * [DNGR](https://github.com/ShelsonCao/DNGR) 85 | * [SDNE](https://github.com/suanrong/SDNE) 86 | * [DRNE](https://github.com/tadpole/DRNE) 87 | * [GraphRNN](https://github.com/snap-stanford/GraphRNN) 88 | * [DCRNN](https://github.com/liyaguang/DCRNN) 89 | * [CNN-GCN](https://github.com/VeritasYin/STGCN_IJCAI-18) 90 | * [ST-GCN](https://github.com/yysijie/st-gcn) 91 | * [Structural RNN](https://github.com/asheshjain399/RNNexp) 92 | * [Bilinear GNN](https://github.com/zhuhm1996/bgnn) 93 | * [PyGAS: Auto-Scaling GNNs in PyG](https://github.com/rusty1s/pyg_autoscale) 94 | * [Soft-mask: Adaptive Substructure Extractions for Graph Neural Networks](https://github.com/qslim/soft-mask-gnn) 95 | * [IVGD: Invertible Validity-aware Graph Diffusion](https://github.com/xianggebenben/IVGD) 96 | * [Graph Attention Multi-Layer Perceptron](https://github.com/PKU-DAIR/GAMLP) 97 | * [Structured Variational Graph Autoencoder](https://github.com/snudatalab/SVGA) 98 | * [GTaxoGym: Taxonomy of Benchmarks in Graph Representation Learning](https://github.com/G-Taxonomy-Workgroup/GTaxoGym) 99 | * [A Survey of Deep Graph Clustering: Taxonomy, Challenge, and Application](https://github.com/yueliu1999/Awesome-Deep-Graph-Clustering) 100 | * [A Comprehensive Study on Large-Scale Graph Training: Benchmarking and Rethinking](https://github.com/VITA-Group/Large_Scale_GCN_Benchmarking) 101 | 102 | ## Platform 103 | * [AliGraph: A comprehensive graph neural network platform](https://github.com/alibaba/graph-learn) 104 | * [DistDGL: Distributed graph neural network training for billion-scale graphs](https://github.com/dmlc/dgl/tree/master/python/dgl/distributed) 105 | * [DGL is an easy-to-use, high performance and scalable Python package for deep learning on graphs](https://github.com/dmlc/dgl) 106 | 107 | ## Applications 108 | * traffic forecasting 109 | * [TrafficStream: A Streaming Traffic Flow Forecasting FrameworkBased on Graph Neural Networks and Continual Learning（IJCAI 2021)](https://github.com/AprLie/TrafficStream) 110 | * [Spatial-Temporal Graph ODE Neural Network(2021 KDD)](https://github.com/square-coder/STGODE) 111 | * [Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting](https://github.com/zezhishao/D2STGNN) 112 | * [MegaCRN: Meta-Graph Convolutional Recurrent Network](https://github.com/StephLee12/MegaCRN) 113 | * [DAAGCN: Dynamic Adaptive and Adversarial Graph Convolutional Network for Traffic Forecasting](https://github.com/juyongjiang/DAAGCN) 114 | * [Combined Dynamic Virtual Spatiotemporal Graph Mapping for Traffic Prediction](https://github.com/Dandelionym/CDVGM) 115 | * [FOGS: First-Order Gradient Supervision with Learning-based Graph for Traffic Flow Forecasting](https://github.com/kevin-xuan/FOGS) 116 | * [Automated Dilated Spatio-Temporal Synchronous Graph Modeling for Traffic Prediction](https://github.com/jinguangyin/Auto-DSTSGN) 117 | * [ST-SSL: Spatio-Temporal Self-Supervised Learning for Traffic Prediction](https://github.com/Echo-Ji/ST-SSL) 118 | * recommendation 119 | * [Graph4Rec: A Universal and Large-scale Toolkit with Graph Neural Networks for Recommender Systems](https://github.com/PaddlePaddle/PGL/tree/graph4rec/apps/Graph4Rec) 120 | * differential privacy 121 | * [differential privacy](https://github.com/tensorflow/privacy) 122 | * link prediction 123 | * [Neo-GNNs: Neighborhood Overlap-aware Graph Neural Networks for Link Prediction](https://github.com/seongjunyun/Neo-GNNs) 124 | * interpretability 125 | * [A Benchmark for Assessing Graph Neural Network Explanations](https://github.com/Mandeep-Rathee/Bagel-benchmark) 126 | * source localization 127 | * [Source Localization VAE](https://github.com/triplej0079/SLVAE) 128 | * Calibration 129 | * [What Makes Graph Neural Networks Miscalibrated?](https://github.com/hans66hsu/GATS) 130 | * Transformer 131 | * [Hierarchical Graph Transformer with Adaptive Node Sampling](https://github.com/zaixizhang/ANS-GT) 132 | 133 | ## Future directions 134 | * Different types of graphs 135 | * Dynamic graphs 136 | * Interpretability 137 | * Compositionality 138 | * Go Deep 139 | * Receptive Field 140 | * Scalability 141 | * Shallow Structure(graph neural net works are always shallow, most of which are no more than three layers.) 142 | * Non-Structural Scenarios 143 | * Green deep learning 144 | * Low resource learning(FSL and ZSL) 145 | 146 | ## GNN application for specific field 147 | * [NLP with GNN](https://github.com/icoxfog417/graph-convolution-nlp) 148 | 149 | ## some related resources 150 | * [Awesome Graph Neural Networks](https://github.com/nnzhan/Awesome-Graph-Neural-Networks) 151 | * [GNNPaper:Must-read papers](https://github.com/thunlp/GNNPapers) 清华大学NLP组 152 | * [GNN相关的一些论文以及最新进展](https://github.com/jdlc105/Must-read-papers-and-continuous-tracking-on-Graph-Neural-Network-GNN-progress) 153 | * [Literature of Deep Learning for Graphs](https://github.com/DeepGraphLearning/LiteratureDL4Graph) 154 | * [Graph-based deep learning literature](https://github.com/naganandy/graph-based-deep-learning-literature) 155 | * [spatio temporal-paper-list(graph convolutional)](https://github.com/Eilene/spatio-temporal-paper-list) 156 | * [Python package built to ease deep learning on graph, on top of existing DL frameworks](https://github.com/dmlc/dgl) 157 | * [对于GNN综述文章的一个整理](https://github.com/ShiYaya/graph) 158 | * [Geometric Deep Learning Extension Library for PyTorch](https://github.com/rusty1s/pytorch_geometric) 159 | * [关于GNN的pytorch模型示例](https://github.com/LYuhang/GNN_Review) 160 | * [Graph Neural Networks for Natural Language Processing](https://github.com/svjan5/GNNs-for-NLP) 161 | * [Graph Neural Network for Traffic Forecasting](https://github.com/jwwthu/GNN4Traffic) 162 | * [self-supervised learning on Graph Neural Networks](https://github.com/ChandlerBang/awesome-self-supervised-gnn) 163 | * [awesome auto graph learning](https://github.com/THUMNLab/awesome-auto-graph-learning) 164 | * [A Survey of Pretraining on Graphs: Taxonomy, Methods, and Applications](https://github.com/junxia97/awesome-pretrain-on-graphs) 165 | * [Reinforcement learning on graphs: A survey](https://github.com/neunms/Reinforcement-learning-on-graphs-A-survey) 166 | * [A Python Library for Graph Outlier Detection (Anomaly Detection)](https://github.com/pygod-team/pygod/) 167 | * [A Python Library for Graph Outlier Detection](https://github.com/pygod-team/pygod/) 168 | 169 | 170 | ## Researchers and Groups 171 | * Data Mining 172 | * [Shirui Pan](https://shiruipan.github.io/) 173 | * [Hongzhi Yin](https://sites.google.com/view/hongzhi-yin/home) 174 | * [Bin Cui](http://net.pku.edu.cn/~cuibin/) 175 | * CV 176 | * [Hengtao Shen](https://cfm.uestc.edu.cn/~shenht/) 177 | --------------------------------------------------------------------------------